Bridging Auscultation and Tiny Machine Learning: A Digital Stethoscope Leveraging Convolutional Neural Networks on an Embedded Device for Organ Sound Analysis
Bibliographic record
Abstract
Traditional auscultation is used to determine certain pathological conditions related to internal organs utilizing cardiac, pulmonary, and intestinal sounds.However, this method relies heavily on the experience of the physician, which leads to non-repeatable subjective diagnosis.Automated analysis can be implemented by digitally recording organ sounds to address this limitation.The proposed system employs a convolutional neural network (CNN) model to determine the auscultated organ and subsequently applies digital filtering to the recorded raw signals based on the organ-specific frequency range.Additionally, the de-noised signals obtained can be transmitted to other smart devices via Bluetooth for further analysis.All the data acquisition, signal processing and learning steps were carried out in an embedded system, the Raspberry Pi 4 board.To achieve organ determination, the input of CNNs is obtained from the raw digital signals in the form of Mel-Spectrograms using the short time Fourier transform (STFT).The obtained time-frequency representations were fed into several pre-trained CNN architectures and compared in performance to a new CNN model derived from FISC-Net.The concept of tiny machine learning was employed in learning to enable real-time, low-power auscultation analysis on a portable and cost-efficient device, ensuring immediate feedback and enhanced patient privacy.The results showed that FISC-Netv1 surpassed other pretrained models by achieving a 90% accuracy rate demonstrating the effectiveness of the proposed system.Furthermore, the application of quantization awareness training reduced the learning model size by 4x without significantly compromising its performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".